I E.

In my research group, we have started prefacing our talks about semantics with "from a robot's point of view". This prevents a lot of questions: "What about problems of determining reference? Aren't all these meanings relative to the robot's view?" and "Aren't these just a robot's reactive and predictive mechanisms? Where is the theory of semantics?". Questions about semantics can be divided into two rough sets: the questions that influence how you build a robot that can maintain rich semantics like a human being can, and the questions external to the robot, such as questions of reference and other mind's semantics. I believe the two often get confused, because the only good examples of minds we have are our own, which happen to both employ meanings and reflect on them. Thus looking at a carrot and uttering "orange", we appear to ourselves to be describing an object of an external world. We can say "I am referring to that carrot", exhibiting that we are not only describing our sensory experience, but also modelling this experience as one of reference to an object. Similarly, we can ask this question about other creatures reacting to the carrot: "Is this reaction about that carrot?" This modelling capability is useful and important to a mind, but it is important to keep in mind that any question about reference is an instance of such modelling, nothing more. Detailed questions about the 'nature of reference' try to pin down a general model of a spectrum of reactions to a carrot, from the poorly self-modelled bunny eating a carrot to the human reflective pondering of one's relation to the carrot. In the end, as with any set of models, some models explain some cases better, some explain more cases, but assuming that there is a truth to be uncovered is a mistake, because the truth depends on what you care about. Thus we steer clear of questions that compare our model of reference (and we deeply care about that one, because we have to implement it) to much broader and more abstract ones. Our robots already refer in many ways, and once they know that they refer, they can provide their own models of reference to all those question-askers to chew on.

I believe Drew McDermott presents a very similar set of observations about semantics and reference in his book. The crux of his book consists of pointing out the importance and power of higher order models to explain how a mind can reflect on its own and the world's workings, leading to consciousness. After defining a computer to be a function from a range to a domain that implements a causal mapping between the two sets, McDermott observes that such a definition does not imply that a computation is about anything (p175), but does imply a replacing option: under suitable circumstances, computers that compute the same functions can replace each other without an impact on the overall system. He goes on to define a notion of symbol that can be found in any computer under a certain decoding. Again, he emphasizes that such a decoding does not concern the aboutness of a symbol (p186). When addressing the issue of semantics, McDermott makes an observation like the one I state above: a system does not need to be aware of the symbols it uses. I agree with his assessment that the human language case confuses the issue because humans are usually taken to be aware of the language symbols they use (note that this is not the case for our robot's saying "orange"). An immediate conclusion is that there is no fixed answer as to what a symbol denotes, because it depends on the observer's model of the act of reference.

After this point, I feel that McDermott would do better to stick to this conclusion rather than confuse the issue by mentioning various theories of informational, causal and evolutionary reference without being clear that these are all second order explanations. There is no doubt that a computational system strongly is part of the world, and thus tightly interacts with it. As Smith says, computation is inherently referential, because it is part of the world, caused and informed by it, and influencing it in return. In fact, viewing McDermott's program in light of Smith's observations on computation might show where McDermott's conclusion should lie. By describing how to find computation in the world, McDermott gives a recipe for how to locate computational subjects. Smith points out that these subjects only come into existence by their modelling of objects, which in turn only become objects by being modelled. However, the crucial insight is that McDermott's and in fact anybody's view of subject and object has to be from another subject's point of view, be it the first subject modelling itself, or a second subject modelling subject and objects. In light of this, there can be no doubt that the first subject refers to objects, and in fact inherently so. However, registering this intentional relation from the view of another subject is again a matter of objectifying the first subject and object as well as their relation. This modelling process can produce various results, depending on what is important to the third subject. Thus, notions like causal past or informational effect are ways of registering this subject-object relation. They can be more or less accurate and informative. McDermott should at this point admit to the fact that he has engaged in a registration process in finding computational systems and objects in the world. I think his conclusion should explicitly be: here is a useful way of finding subjects (as objects) in the world, and here are some possible ways with which we might objectify their intentional nature.

Theories of intentionality like Millikan's and Evan's are similar attempts at suggesting useful ways of finding subjects, objects and their relations in the world. What is important to note, and how McDermott should defend his view against theirs, is that they discretize the world into objects in a different manner. Millikan, for example, treats subjects as extending in time back through their evolutionary history to be able to explain reference with appeal to it. Similarly, her objects must have strong temporal components so that she can objectify the relation between subjects and objects as consisting of the subject evolving abilities to register (in Smith's terms) objects. To us as researchers, for example, this way of carving up the world is not as intuitively useful because our robots currently lack any significant interaction history and our design process is their evolutionary history, leaving the decision as to what they refer to utterly up to us. Second order models of subjects range widely through Philosophy, from Cartesian points to Putnamian populations. Restating them while making clear that they are second order models that commit to ways of carving up the world into the necessary ingredients for a theory of intentionality goes a long way to resolving arguments about perceived facts of the matter.

In summary, McDermott's notion of second order modelling is a useful and exciting one, but he fails to carry its implications to their natural end. He provides a nice recipe for finding computational subjects in the world that may themselves be able to engage in second order modelling of their own activities, but fails to acknowledge that he, McDermott, engages in second order modelling throughout the book. As a result, he makes the common mistake of claiming to be stating facts of the matter about the modelled phenomena. Rather, he should be claiming that he is communicating a useful second order model of first order subjects, objects and subject-object relations. This provides a natural defence against other theories of reference, because they can be seen as making different commitments as to how to find subjects and objects in the world, and should be evaluated as such.

I B.

I will consider the proxytype theory by Prinz in two parts: a useful attempt to combine the various concept theories into a coherent whole, and (from my point of view) an unnecessarily extreme 'empirical' position that ends up positing unfounded (and perhaps empty) restrictions.

First, about the useful bits. The regress problem stated on p82 is very real and impacts much of current AI. In our lab, we have a number of people working on what they call commonsense knowledge (Marvin Minsky's term). However, in many of their current instantiations this knowledge takes the form of thousands of English statements (http://commonsense.media.mit.edu/). This knowledge is useful because it can be mined for correlations. However, it cannot be useful to the machine itself, because it lacks links to anything but more English statements. The answer currently is to provide thin links, usually in terms of keywords, to the content of interest, for example pictures. This directly leads to the common semantic poverty exhibited by such machines. This is the same problem facing all of the theories proxytype theory is meant to replace, because they do not acknowledge the dire need for what my research group calls 'semantic grounding', and which leads Prinz to posit the perceptual basis for proxytype theory.

On the other hand, as Prinz realizes, theories like prototype theory or theory theory contain important kernels of insight that should be retained by a new theory. His response is exactly our response, namely to attempt to ground out and combine these theories by linking them to perception and action. This leads him to propose long term memory networks that store perceptual representations and their relations based on complex sensory and causal analysis. They are divided into varying levels of visual and other perceptual expectations, dynamically groupable into concepts by their link structure. The description of these structures (and many other aspects of the book) are at a level of detail that make it impossible to assess their validity because the design challenges in their implementation are too vast.  I will have more to say about this problem below. However, there are clear links to other theories of concepts, such as the templates Millikan introduces for substance concepts. Clearly, the perceptual representations Prinz proposes are organized in this way, as becomes clear from his discussions of how they are applied and what questions they make one ask (as Millikan puts it). These are valuable strategies for quick substance detection and concept acquisition. The necessity for such templates in modelling human cognition has recently been introduced to Bayesian modelling of causal reasoning by Tenenbaum. He shows how one shot causal generalizations by children can be explained with strong biases over the possible causal structures of physical objects, exactly like Millikan and Prinz predict. Proxytypes themselves, finally, are temporary instantiations of the relevant parts of the background memory network to perform online perceptual detection and reasoning in a given context.

Prinz goes on to defend his proxytypes against some of the common attacks on other theories of concepts. He first tackles the applicability of proxytypes to various 'harder' concepts. One such concept is that of causality, and I want to supplement his arguments for the perceptual basis of causality concepts with the observations that there is a rapidly growing literature based on Pearl's notion of causal networks that is successfully applying these models to human adult and infant causal reasoning. Other concepts are more abstract ones, like that of democracy, and again I believe his explanation that these concepts can be traced back to their perceptual bases through networks of associations and representations of experiences. Again, he could avail himself of much stronger evidence from Cognitive Science, such as Lera Boroditsky's work on the reality of a re-use of spatial reasoning systems for temporal reasoning in humans. These experiments hint at the power of re-use of reasoning strategies at varying levels of abstraction and for varying problems. I am including some of my observations on this phenomenon in a separate essay I wrote some time ago below. Strongly related to this notion of re-use of reasoning strategies and transform of problems into easier to handle representations is Dennett's notion of tools as discussed in the Clapin volume. Tools under this notion undergo evolution and refinement and are part of making both the mind's internal problems and its external world easier to deal with and reason about. Reasoning strategies over, for examples, spatial representations are a good example of this, providing the common ground many problems can be rephrased into. This surely applies to Prinz's re-representation of modal content in other modalities' representational paradigms as well.

To supply a notion of reference, Prinz first proposes a causal theory of reference. As I argued above a theory of reference is necessarily relative to a carving up of the world into subjects and objects. Prinz seems to mostly care about his subject's second order models of their own reference behaviours (e.g. p248: "We take our natural-kind concepts to pick out unique natural kinds.") In light of this, the stipulation of a true referent even in the case that the subject does not model this reference (i.e. some of the problems raised by twin earths and such) seems unnecessary. Rather, Prinz could simply report on the subject's model of reference (rather than Prinz's), which may include belief in a fixed referent, rather than pretending there is an external answer to the matter. McDermott seems to make this important distinction. As with Prinz's 'strict' empiricism, this choice seems mainly determined by arguments against others' irrelevant objections, and I think he would do better to keep his story about subjects' models of reference than external desiderata. He suddenly switches to this view when introducing nominal content. I am not arguing against the subject's notion of two types of reference. We certainly seem to believe in it. I am arguing against making this distinction an external fact of the matter and imposing distinctions even in cases where the subject's can't impose them.

Having just built a system that performs complex semantic combinations of visually based concepts, Prinz's claims about the compositionality of proxytypes are close to my heart. However, both his general observations and the RCA approach to conceptual combination leave out necessary details and cover only a small part of the ground. My system, for example, formed concepts for expressions like "the green one in front of the three purple ones", referring to a scene of objects. Identifying a visually salient group of three purple ones involves measuring its spatial relation to potential referents and deciding on a plausible candidate. This seems to me to be a fine example of conceptual combination that involves spatial features measured between objects, and selection of concepts based on visual and language concepts. And, believe me, the devil is in the detailed design. In short, I am not sure how general or useful a theory of conceptual combination at the level presented by Prinz is. I do, however, agree that concept combination is not an unsolveable problem for proxytypes.

In contrast with my general agreement with Prinz's proxytype theory, I find that he radicalizes it unnecessarily to distinguish it from other approaches. Thus, to distinguish it from any language-of-thought-like approach, he insists that all representations are perceptually based. He extends this claim to imply that we employ no amodal mental representations at all. However, he does admit that representational content needs to be translated between modalities via 'rules' (p135). This leaves me unclear as to when a representation could become amodal. Take Ripley, our robotic arm, for example. It has a proprioceptive representation, a force-based representation and a visual representation, using all three to model itself as well as objects around it in a physics simulator in its brain. This simulator allows it to take other mind's points of view, to track objects that are not currently in view, to distinguish self-moving from other objects and to simulate experiments without having to perform them. Is the representation used in the simulator modality specific? It is certainly not a language of thought or any general purpose reasoning system. At the same time, it seems hard to ascribe this representation to any specific modality, even though it is clearly perceptually inspired. Is it therefore not a valid candidate for a proxytype theory? That conclusion seems overly restrictive. Prinz simply seems to underestimate the complexity involved in the 'translation' he posits to exist between modal representations. Such translation is one of the hard challenges AI faces in the immediate future, and seldom will it be simple translation. Rather, it seems likely that minds maintain reasoning systems and associated representations supported by several, if not all, modalities. They are problem-type and often task specific, and constitute translation between modalities, just like Ripley's simulator 'translates' visual information into tactile expectations. Prinz would seem to be better off claiming that there is no pervasive amodal representation that is used to represent everything, rather than committing to modality specific representations.

The pretended simplicity in 'translation' also leaves Prinz unable to address the higher order modelling requirements of theories of consciousness such as Dennett's and McDermott's. It seems that a model as powerful and complex as the one our minds maintain of our selves requires still far more cross-modal representations, if they can still be called modal at all, than Ripley's simulator does. I personally believe that some of the same modelling and representation strategies used for perceptual representations get reused for higher order modelling efforts (I hope to provide a more detailed explanation of this view in the near future), but that does not imply that it is useful to see higher order models as perceptual representations themselves. Still, that does not make them general reasoning representations, and thus commits to nothing like a language of thought.

Overall, then, Prinz presents a coherent if high-level story of how concepts must be perceptually grounded and of some of the ways existing theories of concepts can be combined into such a view. However, at several points, such as his concern with an absolute theory of reference and his extreme empiricism, he seems to be more trying to respond to opposing theories than to provide an internally coherent theory of proxytypes. This is understandable as he has to place himself in a tradition of philosophical theories, but disappointing because it detracts from the real issues faced by someone who wants to confirm or implement Prinz's theory.

II A.

I have touched on my appreciation of Smith's proposal several times already at this point. Let me attempt to clearly spell out what I take it to be and why I appreciate it.

I believe Smith's insights stem from observations every computer scientist with an interest in the real world should arrive at sooner or later. The first lesson of software engineering is that deciding how to carve up the world in order to represent it is hard, purpose-dependent and in an important way a form of art. Immense foresight is required to produce a useful, implementable and extendible representation. Inevitable failure results in brittleness, malfunction and conceptual breakdown. At the same time, the joy of creating a beautiful solution is hard to explain to non-programmers.

Two common mistakes in thinking about representation are anthropocentrism and anthropomorphism. People (including philosophers) tend to assume that tasks that are easy for humans, such as representing a world with objects in it, are easy in general. This faulty view is compounded by that fact that they then assume that any system they can see as human produces the same solution they produce (if they even acknowledge that there is a problem.) Anyone who has ever built a robot knows that many of the problems that most people are unaware of, such as deciding that one blob of similar pixel values corresponds to another blob of similar pixel values at the next timestep (or that there even is a blob of similar pixel values), are in reality hard problems. At the same time, current vision solutions to this problem are an immense simplification, not so much of the vision problem itself, but of the other knowledge and abilities people employ in tracking objects. Smith carries this insight to the conclusion that any subject representing any object must be facing these same difficulties, and that it therefore is a mistake to assume the world to be discretized into subject and object a priori.

Thus starting by throwing out objects altogether, Smith sketches a way to arrive back at the subject-object distinction by acknowledging the work the subject needs to do to distance itself from the object. One of the crucial elements in the story is that of flex and slop, the notion that the notion of distance only exists because phenomena in our world are only locally effective. This allows the effective relation between two regions in the undiscretized world to be severed, causing the regions to be separated by distance. To become subject and object, the subject must engage in registration behaviour, that is, it must attempt to bridge this severing of the effective link. The main way to create such a bridge is that of internalizing a representation of the object into the subject. Smith sees this as a dynamic process in which the subject must stabilize the object from a variety of perspectives, finally yielding a dynamic representation of the object, objectifying the object and subjectifying the subject in the process. Of course, this all requires a parallel effort on part of the object that must let itself be stabilized in this way. The subject can now exploit this representation to bridge the severing of the effective link with the object, tracking the object both when it is in effective reach and when it is not. This interplay of non-effective representation supplemented by effective registration constitutes intentionality.

From my perspective, Smith's story is valuable in several ways. First, it identifies some of the assumptions computer scientists, cognitive scientists and philosophers make, and provides at least one theory of how one might arrive at these assumptions. One thing this did for me, for example, is to make me less queasy about the step from perception to objects. Before Smith, estimating a Gaussian to track colour blobs and having the robot decide that there indeed is an object and to call it, say, 13, seemed like a major ontological step. With Smith, I now like to view this as a minor step on a continuum from a non-discrete metaphysical flux to an artificial mind that can register objects. Obviously, the mechanisms are always up for revision, but I can tell a better story of why and how objectification is the right thing for a robot to spend energy on.

Second, Smith's notion of subject-object distance and the function of registration lets me re-evaluate where on the scale from metaphysical flux to distinct subject and object our robots fall. At the same time, I believe that an evaluation of this sort must involve second order modelling in several ways, and thus connect to second-order model theories of consciousness that Smith insufficiently weaves into his separation story. He hints at a version of this on page 240, but it rarely enters in later parts of the book. Smith does a good job of accounting for how a dynamic registration process can turn metaphysical flux into subject and object. It seems that this account can be extended to include a theory of how the subject itself can become an object. Obviously, another subject can register that first subject as an object. However, more interesting is the case in which the second subject is the same as the first subject. On page 240 Smith seems to claim that this necessitates an inversion of the registration processes of the subject. Rather, I simply propose to spin the same registration story one more time, and register the subject as an object.

How does this version of the story go? Much the same way as it did before. The subject must now stabilize itself through much the same efforts through which it registers other objects. It must let go of the effective relation between itself as subject and itself as object, and compensate for other elements through which it obtained access to itself. In the case of spatial representations, for example, this means that it must shift the representation of itself from an ego- to an allocentric representation, to view itself as an object in the world. This is exactly a point Evans make in Varieties of Reference, and that Grush spells out in Self, World and Space. In short, the subject must spend energy to model itself in order to track itself as an object through space and time. This is the exact requirement for a second order model of consciousness as posed by McDermott and Dennett. A subject will be conscious in the case that it fully registers itself as an object.

This points to aspects of our work in which the subject, in this case one of our robots, lacks distance both from the objects it attempts to register and from itself as an object. Our robots generally require a relatively effective link to external objects. While they have representations, these representations so far largely stand in direct effective relation to the current world around them. The representation separates them from the objects (after all, they can talk about them), but insufficiently to compensate for many of the things we would like them to stabilize against. Especially poor in comparison to human subjects is the link to language. Human object descriptions usually imply a conscious effort, an acknowledgement by the subject that it knows itself as an object and that it stands in a certain relation to other objects. In our robots, object descriptions imply no such thing, but are rather directly (effectively) caused by perceptual representations. This seems like a fundamental lack of separation that leads to brittle behaviour when our robots represent objects and reflect on them.

As I have pointed out earlier, and perhaps also in connection to Smith's own thoughts on page 240, registration of the subject as an object is a necessary step for others, including the subject itself, to assess its own registration behaviour. Philosophers must engage in this type of registration when they produce theories of reference (lack of acknowledgement of this fact is what Smith calls an inscription error, discussed in length in the Clapin volume), and without stating these assumptions comparing theories of reference becomes a somewhat arbitrary endeavour. Objectifying the reference relation itself, on the other hand, requires registering and tracking it as well, which can be done in different ways and with different degrees of success. This is a problem both for the subject itself and for other subjects objectifying the first subject, especially when the subjects are performing similar feats of registration as they are trying to model in others (or themselves). The resulting confusion of subjective, objective and second-order views is no surprise when they are not clearly distinguished as such.

III

I would like to address the various views on Molyneux's question presented in a number of works on the reading list: Grush, Evans, Millikan and Prinz come to mind. In doing so I will draw my only conclusions that lead me to a firm answer of "depends".

Let me begin with Grush's observations in Self, World and Space as well as his paper in the Evans collection. I remain unconvinced that the skill theory of grasping content should rely on motor commands like it does. Motor commands and their effects on bodies and sensory input are invaluable tools for human beings. Why is that? I believe this is due to the fact that human beings perform causal analysis, and that such analysis requires being able to perform what Pearl calls interventions. I do not think that interventions necessarily need to be motor commands.

Pure observation, if such a thing exists, tells one about correlations of events in the world. From this, and learning programs do this often, it is possible to compute information theoretic measures that indicate how much one event tells an observer about occurrence of another and vice versa. The types of coordination Grush proposes in Self, Mind and Space are examples of such measures that can lead to aligning representation topographies. This type of analysis is in principle independent of the choice of dimensions along which observations are represented. However, this choice may be limited by how we have evolved to represent observations. Evolution might have decided to collapse some possible dimensions but not others, by, for example, the number of types of cones in the eye. Such collapsing does not necessarily mean that the interesting topology of the observations is not recoverable. Collapsing of dimensions and its possible topology preservation are frequent topics of work in computation modelling and machine learning. This seems to be Grush's point about the first version of the hypothesized Fourier, who has to have hearing that preserves the topological features of interest to perform informational analysis on them. The ease of relating a pulsating visual representation to a pulsating audible representation is simply that there can be a strong bias to correlating a temporal dimension for this purpose. I am thus by no means claiming that Grush's story of correlating spatial maps to arrive at allocentric spatial representations is uninteresting. In fact, it is exactly this type of correlation that produces a new representation that I was thinking of in my complaint's about Prinz's modal representations above. It is also exactly this type of mapping the Ripley, our robot, performs to translate its sensory information into the spatial world and points of view of its physics simulator. However, I do not think that the argument Grush attempts to make from spatial representation is necessary.

The pure observational case is limited in that it only allows certain types of causal analysis.  The simplest example is that in the absence of other information, neither of two correlated events can be said to be causing the other, nor can it be said that they are not both caused by a third. Sometimes, but not always, the direction of causality can be determined by observing other events' correlation with the original two. Being able to cause one of the events to happen lets one easily determine the causal relation. That is, I believe, what Evans and Grush are trying to get at with a skill based theory of content. In the absence of action, one's model of the world is necessarily impoverished, because not all causal relations can be established. The part of the argument that I am ready to believe is that a causal analysis is necessary for a sense of self. As Grush points out, and as I believe Smith would concur, one has to be able to represent objects as independent of oneself, to stabilize them as objects in the world, and the only way to do that is by a causal analysis. The parts of the argument that I think are unnecessary are the appeals to motor commands and space. It is true that these are the primary tools in the causal analysis a human being performs to represent objects as objects, but they are not essential ingredients of performing the type of causal analysis Grush suggests. A notion of distance, as put forward by Smith, is necessary, in that the subject must maintain registration across non-effective links. However, the overtones of a metric space, if not the metric space, that a mention of motor skills invokes is a purely human predicament. Imagine, for example, a virtual agent interacting with other agents in a network. It can perform actions, interact with virtual objects (for example, buying you a copy of Varieties of Reference) as well as other agents (including humans) and perform a causal analysis that establishes its distance to other objects and perhaps a sense of self, by examining its effective reach and bridging it.

Abandoning the notion that space is essential for grasping contents, it is still true that human beings in general do perform a causal analysis using motor commands and space as described. Part of this analysis establishes a correlation (or 'translation', as Prinz says) between visual perception, motor commands, and perception in other modalities. As argued before, I disagree with Prinz that this analysis may not create an internal cross-modal representation of space in the process of analysis of sensory inputs and action outputs. Can this happen in the absence of vision? Absolutely so. There are enough other modalities with spatial content to be correlated and causally analysed to make sure that a blind person arrives at a notion of space. I don't think this notion is necessary for all senses of self or grasping of content, but I do think it is necessary for something close to the human self and grasp of content. I also believe that due to the adaptiveness of the human brain a formerly blind person can learn to correlate the new visual perceptions with its established spatial model. My answer thus is "depends" only because I cannot say how quick such learning would be and if it is possible in all cases (both seem to depend on the nature of the blindness in the first place), and because I do not think that an internal representation of space is necessary for any intelligent, conscious mind. I just believe it is necessary for humanlike ones.